Scatter plots in Power BI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ visualization

intermediate

Choosing the correct axis for a scatter plot

You want to create a scatter plot in Power BI to analyze sales performance. You have two numeric columns: Sales Amount and Number of Customers. Which should you place on the X-axis and which on the Y-axis for best practice?

A X-axis: Sales Amount, Y-axis: Number of Customers

B X-axis: Sales Amount, Y-axis: Sales Amount

C X-axis: Number of Customers, Y-axis: Sales Amount

D X-axis: Number of Customers, Y-axis: Number of Customers

💡 Hint

Think about which variable is independent and which is dependent.

Number of Customers is usually the independent variable (X-axis), and Sales Amount depends on it (Y-axis). This helps see how sales change with customer count.

❓ dax_lod_result

intermediate

Calculate average sales per customer for scatter plot

You want to add a measure to your scatter plot that shows Average Sales per Customer. Which DAX formula correctly calculates this?

A Average Sales per Customer = SUM(Sales[Sales Amount]) * SUM(Sales[Number of Customers])

B Average Sales per Customer = AVERAGE(Sales[Sales Amount]) / AVERAGE(Sales[Number of Customers])

C Average Sales per Customer = SUM(Sales[Sales Amount]) - SUM(Sales[Number of Customers])

D Average Sales per Customer = DIVIDE(SUM(Sales[Sales Amount]), SUM(Sales[Number of Customers]))

💡 Hint

Think about total sales divided by total customers.

DIVIDE(SUM(Sales[Sales Amount]), SUM(Sales[Number of Customers])) safely calculates average sales per customer.

❓ data_modeling

advanced

Modeling data for scatter plot with categories

You want to create a scatter plot showing sales amount vs number of customers, colored by Region. Your data model has a Sales table and a separate Regions table. What is the best way to model this for the scatter plot?

A Create a relationship between Sales[RegionID] and Regions[RegionID], then use Regions[RegionName] as legend in scatter plot.

B Merge Sales and Regions tables into one table before creating the scatter plot.

C Use Sales[RegionID] directly as legend without any relationship.

D Create a relationship between Sales[Sales Amount] and Regions[RegionName].

💡 Hint

Think about how tables connect in Power BI.

Creating a relationship between Sales and Regions tables on RegionID allows using RegionName as legend properly.

🔧 Formula Fix

advanced

Identify the error in scatter plot measure

You wrote this DAX measure to calculate average sales per customer for a scatter plot:

Avg Sales per Customer = SUM(Sales[Sales Amount]) / COUNT(Sales[CustomerID])

But the scatter plot shows incorrect values. What is the likely problem?

Power BI

Avg Sales per Customer = SUM(Sales[Sales Amount]) / COUNT(Sales[CustomerID])

A SUM cannot be divided by COUNT; use AVERAGE instead.

B COUNT counts all rows including duplicates, so it undercounts customers; use DISTINCTCOUNT instead.

C The measure should use MAX instead of SUM for Sales Amount.

D The formula is correct; the problem is with the scatter plot settings.

💡 Hint

Think about counting unique customers.

COUNT counts all rows, including duplicates. DISTINCTCOUNT counts unique customers, which is needed for average per customer.

🧠 Conceptual

expert

Interpreting scatter plot clusters in sales data

You created a scatter plot with Sales Amount on Y-axis and Number of Customers on X-axis. You notice three distinct clusters of points:

• Cluster 1: Low customers, low sales
• Cluster 2: High customers, medium sales
• Cluster 3: Medium customers, high sales

What is the best interpretation of these clusters?

A Cluster 1 represents small stores, Cluster 2 represents stores with many customers but low sales per customer, Cluster 3 represents stores with fewer customers but high sales per customer.

B Cluster 1 represents stores with high sales efficiency, Cluster 2 represents stores with low customer count, Cluster 3 represents stores with average sales and customers.

C Clusters show random data with no meaningful pattern.

D Cluster 1 and 3 represent the same type of stores, Cluster 2 is an outlier.

💡 Hint

Think about sales per customer in each cluster.

Cluster 1 has low customers and sales, likely small stores. Cluster 2 has many customers but medium sales, so sales per customer is low. Cluster 3 has medium customers but high sales, so sales per customer is high.